Digital goods representation based upon matrix invariants using non-negative matrix factorizations

Abstract

Described herein is one or more implementations that produce a new representation of a digital good (such as an image) in a new defined representation domain. In particular, the representations in this new domain are based upon matrix invariants. More particularly still, the specific matrix invariants described herein include non-negative matrix factorizations (NMF).

1 . A processor-readable medium having processor-executable instructions that, when executed by a processor, performs a method comprised of representing digital goods in a defined representation domain, wherein such representation is based upon matrix invariants, wherein the matrix invariants include non-negative matrix factorizations (NMF).  
   
   
       2 . A medium as recited in  claim 1 , wherein the method further comprises extracting robust pseudo-random features of the digital goods, wherein the features are within the defined representation domain.  
   
   
       3 . A medium as recited in  claim 1 , wherein the digital goods is selected from a group consisting of a digital image, a digital audio clip, a digital video, a database, and a software image.  
   
   
       4 . A computing device comprising: 
 an audio/visual output;    a medium as recited in  claim 1 .    
   
   
       5 . A processor-readable medium having processor-executable instructions that, when executed by a processor, performs a method facilitating protection of digital goods, the method comprising: 
 obtaining a digital good;    partitioning the good into a plurality of regions;    calculating statistics of one or more of the regions of the plurality, so that the statistics of a region are representative of it, wherein the statistics calculated are based upon matrix invariants, wherein the matrix invariants include non-negative matrix factorizations (NMF).    
   
   
       6 . A medium as recited in  claim 5 , wherein at least some of the plurality of regions overlap.  
   
   
       7 . A medium as recited in  claim 5 , wherein the partitioning comprises pseudo-randomly segmenting the good into a plurality of regions.  
   
   
       8 . A medium as recited in  claim 5 , wherein the digital goods is selected from a group consisting of a digital image, a digital audio clip, a digital video, a database, and a software image.  
   
   
       9 . A medium as recited in  claim 5 , wherein the method further comprises producing output comprising the calculated statistics of one or more regions.  
   
   
       10 . A modulated signal generated by a medium as recited in  claim 9 .  
   
   
       11 . A computer comprising one or more processor-readable media as recited in  claim 5 .  
   
   
       12 . A method comprising: 
 obtaining a digital good;    partitioning the good into a plurality of regions;    extracting robust features from the plurality of regions, wherein the features are based upon matrix invariant non-negative matrix factorizations (NMF).    
   
   
       13 . A method as recited in  claim 12 , wherein the extracting act is characterized by calculating statistics of one or more of the regions of the plurality, so that the statistics of a region are representative of it, wherein the statistics calculated are based upon the matrix invariant NMF.  
   
   
       14 . A method as recited in  claim 12 , wherein at least some of the plurality of regions overlap.  
   
   
       15 . A method as recited in  claim 12 , wherein the partitioning comprises pseudo-randomly segmenting the good into a plurality of regions.  
   
   
       16 . A method as recited in  claim 12 , wherein the digital goods is selected from a group consisting of a digital image, a digital audio clip, a digital video, a database, and a software image.  
   
   
       17 . A method as recited in  claim 12 , wherein the method further comprises producing output comprising the robust features of one or more regions.